• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.891-893
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• 1997

In this paper an intelligent power transformer protective relaying algorithm based on Fuzzy Decision-Making is presented. The introduced protection algorithm contains several internal fuzzy rule-bases including bpa(Basic Probability Assignment: m) which are subject to off-line pre-installation by the analysis of the transformer transient characteristics for detecting the internal fault. Dempster-Shafer's rule of combination is used for the inference method with rules to decide the situation of a transformer, The proposed algorithm immunes to the saturation of transformer, inrush conditions, over excitation, and external fault. The included results of testing show practically sufficient sensitivity and selectivity of the proposed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3965-3982
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• 2015

Event detection is one of the key issues in many wireless sensor network (WSN) applications. The uncertainties that are derived from the instability of sensor node, measurement noise and incomplete sampling would influence the performance of event detection to a large degree. Many of the present researches described the sensor readings with crisp values, which cannot adequately handle the uncertainties inhered in the imprecise sensor readings. In this paper, a fault-tolerant event detection algorithm is proposed based on Dempster-Shafer (D-S) theory (also called evidence theory). Instead of crisp values, all possible states of the event are represented by the Basic Probability Assignment (BPA) functions, with which the output of each sensor node are characterized as weighted evidences. The combination rule was subsequently applied on each sensor node to fuse the evidences gathered from the neighboring nodes to make the final decision on whether the event occurs. Simulation results show that even 20% nodes are faulty, the accuracy of the proposed algorithm is around 80% for event region detection. Moreover, 97% of the error readings have been corrected, and an improved detection capability at the boundary of the event region is gained by 75%. The proposed algorithm can enhance the detection accuracy of the event region even in high error-rate environment, which reflects good reliability and robustness. The proposed algorithm is also applicable to boundary detection as it performs well at the boundary of the event.